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This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual DBS Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed. Updates on ongoing use and developments of DBS for the treatment of Parkinson's disease, essential tremor, Alzheimer's disease, depression, post-traumatic stress disorder, obesity, addiction were presented, and progress toward innovation(s) in closed-loop applications were discussed. Each section of these proceedings provides updates and highlights of new information as presented at this year's international Think Tank, with a view toward current and near future advancement of the field.
Background
Pneumonia frequently complicates stroke and has amajor impact on outcome. We derived and internally validated a simple clinical risk score for predicting stroke-associated pneumonia (SAP), and compared the performance with an existing score (A\(^{2}\)DS\(^{2}\)).
Methods and Results
We extracted data for patients with ischemic stroke or intracerebral hemorrhage from the Sentinel Stroke National Audit Programme multicenter UK registry. The data were randomly allocated into derivation (n=11 551) and validation (n=11 648) samples. A multivariable logistic regression model was fitted to the derivation data to predict SAP in the first 7 days of admission. The characteristics of the score were evaluated using receiver operating characteristics (discrimination) and by plotting predicted versus observed SAP frequency in deciles of risk (calibration). Prevalence of SAP was 6.7% overall. The final 22-point score (ISAN: prestroke Independence [modified Rankin scale], Sex, Age, National Institutes of Health Stroke Scale) exhibited good discrimination in the ischemic stroke derivation (C-statistic 0.79; 95% CI 0.77 to 0.81) and validation (C-statistic 0.78; 95% CI 0.76 to 0.80) samples. It was well calibrated in ischemic stroke and was further classified into meaningful risk groups (low 0 to 5, medium6 to 10, high 11 to 14, and very high >= 15) associated with SAP frequencies of 1.6%, 4.9%, 12.6%, and 26.4%, respectively, in the validation sample. Discrimination for both scores was similar, although they performed less well in the intracerebral hemorrhage patients with an apparent ceiling effect.
Conclusions
The ISAN score is a simple tool for predicting SAP in clinical practice. External validation is required in ischemic and hemorrhagic stroke cohorts.
Background
Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.
Results
Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.
Conclusion
Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.